Department of Psychology.
Psychol Methods. 2020 Apr;25(2):243-258. doi: 10.1037/met0000226. Epub 2019 Jul 11.
Instrumental variable methods are an underutilized tool to enhance causal inference in psychology. By way of incorporating predictors of the predictors (called "instruments" in the econometrics literature) into the model, instrumental variable regression (IVR) is able to draw causal inferences of a predictor on an outcome. We show that by regressing the outcome y on the predictors x and the predictors on the instruments, and modeling correlated disturbance terms between the predictor and outcome, causal inferences can be drawn on y on x if the IVR model cannot be rejected in a structural equation framework. We provide a tutorial on how to apply this model using ML estimation as implemented in structural equation modeling (SEM) software. We additionally provide code to identify instruments given a theoretical model, to select the best subset of instruments when more than necessary are available, and we guide researchers on how to apply this model using SEM. Finally, we demonstrate how the IVR model can be estimated using a number of estimators developed in econometrics (e.g., 2-stage least squares regression) and point out that the latter is simply a multistage SEM estimator of the IVR model. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
工具变量方法是增强心理学中因果推理的一种未充分利用的工具。通过将预测器的预测器（在计量经济学文献中称为“工具”）纳入模型中，工具变量回归（IVR）能够对结果上的预测器进行因果推断。我们表明，通过将因变量 y 回归到预测器 x 和预测器到工具上，并对预测器和因变量之间的相关干扰项进行建模，如果在结构方程框架中不能拒绝 IVR 模型，则可以对 y 上的 x 进行因果推断。我们提供了一个使用 ML 估计的教程，该方法在结构方程模型（SEM）软件中实现。我们还提供了代码，用于在给定理论模型的情况下识别工具，在有多余工具的情况下选择最佳工具子集，并且指导研究人员如何使用 SEM 应用该模型。最后，我们演示了如何使用计量经济学中开发的许多估计器（例如两阶段最小二乘回归）来估计 IVR 模型，并指出后者只是 IVR 模型的多阶段 SEM 估计器。（PsycINFO 数据库记录（c）2020 APA，保留所有权利）。
